Publication date: Jul 02, 2023
A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99. 0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24. 03%, 5. 0%, and 24. 10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99. 0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41. 72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.
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| Concepts | Keywords |
|---|---|
| Basel | artificial intelligence |
| Informatics | COVID-19 |
| Models | deep learning |
| Pandemic | FaceMask |
| MobileNetV2 | |
| pandemic | |
| SARS CoV-2 | |
| surveillance | |
| World Health Organization |